The use of spectroscopy techniques for determining certain critical quality parameters in a fluid, for example wine, has been successful, but only when the analyte in question is elemental (potassium, calcium or iron). For example, U.S. Pat. No. 8,794,049B1 describes a system for the online monitoring of certain parameters of interest in the control of the wine fermentation process. In this case, the pressure created by the carbon dioxide flow which emanates as a result of the fermentation activity is monitored. In turn, patent application U.S. 2002/0023849A1 describes a method for detecting the ethanol presence in samples of a fluid using a non-porous PVC barrier without plasticizing interposed between the sample and an ethanol detector.
In recent years, near-infrared spectroscopy (NIRs) has been transformed into an alternative to traditional physical, chemical and chromatographic methods. For example, within the winegrowing sector, near-infrared technology (NIRs) allows the measuring of quality parameters of the wine required for the control of the fermentation processes. It is very useful to be able to carry out an automatic control for measuring the quality of the product and determining whether corrective intervention is necessary during the fermentation in order to maintain the quality. Furthermore, NIRs has the added advantage of being capable of quantifying multiple parameters at the same time using a single spectrum. The applications and measuring parameters for other sectors are also very varied from quality control of milk to the ripening percentage of fruits and vegetables. The spectral range of NIR extends from the highest wavelengths of the visible end (around 780 ηm) to 3000 ηm (13000 cm-1 to 3300 cm-1).
The advantages offered by NIRs technology are principally based on the speed of the processing and ease of use and handling, principally due to the scarce need to pre-process the analyte to be analyzed. In spite of the fact that a certain inversion in systems for online monitoring integrated into the production processes is involved, NIR spectroscopy has taken advantage over the rest of the analytic methods mainly due to the capacity thereof to carry out fast, non-destructive measurements both of solid compounds and liquids. However, compared with FTIR spectroscopy, NIR is characterized by its low specificity since in many cases the obtained bands are overlapping and have low sensitivity due to the fact that the large variations of the properties produce small variations in the visible NIR spectrum. Therefore the use of multivariate calibration techniques is necessary in order to be able to correlate the useful information of the spectrums obtained with the reference measurements obtained in the laboratory. Multivariate calibration is a discipline within chemiometry (a discipline which uses mathematical and statistical methods for designing and selecting optimal measuring and experimental procedures in order to provide the maximum amount of chemical information by means of the chemical data analysis) essential in NIR spectroscopy due to the complexity of the signal obtained by this technique. The objective of multivariate calibration is to search for the relation between a series of indirect measurements which are easy to obtain and a series of direct measurements from the laboratory which are expensive or require intensive labor. That is to say, to create a good calibration model such that the parameters measured in the laboratory by means of expensive techniques can be determined quantitatively in a fast and economic manner based on measurements carried out with cheaper methods.
The development of a multivariate calibration model is a complex process wherein the principal objective is to relate the N experimental variables (spectroscopy data) against one or various known properties of the samples. The typical strategy to be followed in the development of a multivariate calibration model consists of the following steps: selecting the sample group; determining the reference parameter; obtaining the analyte signal; processing the data; generating the calibration model; and validating.
On the other hand, the large number of spectral variables which are in the majority of the spectral data groups usually makes it difficult to predict a dependent variable. Furthermore, the existence of a large number of samples and variables means that the calibration process can be very costly in terms of time. It is therefore necessary nowadays to use the selection of predictor variables with the aim of not only saving time in the calibration, but also in order to eliminate those predictor variables (wavelength) which do not contain relevant information or which can damage the final result of the multivariate calibration. The exclusion of the irrelevant variables improves the characteristics of the model in terms of accuracy and robustness. In addition, the selection of variables is a very useful tool for improving the robustness of the multivariate calibration models. By means of the selection of variables it is possible to eliminate those variables which do not provide useful or relevant information, thereby obtaining an improved calibration model in terms of accuracy and robustness.
U.S. patent application U.S. 2010/0297291A1 describes an analysis method of the visible/near-infrared spectrum for monitoring certain parameters of the wine fermentation process. For this, laboratory measuring devices are used which are not integrated into the production process itself. Specifically, the method is developed on a grape sample.
In turn, Chinese patent application CN103234923 proposes an online monitoring method of the sugar content in a wine during fermentation by means of spectroscopy techniques.